30-day in-hospital stroke case fatality and significant risk factors in sub-Saharan–Africa: A systematic review and meta-analysis

Existing studies investigating 30-day in-hospital stroke case fatality rates in sub-Saharan Africa have produced varying results, underscoring the significance of obtaining precise and reliable estimations for this indicator. Consequently, this study aimed to conduct a systematic review and update of the current scientific evidence regarding 30-day in-hospital stroke case fatality and associated risk factors in sub-Saharan Africa. Medline/PubMed, Cumulative Index to Nursing and Allied Health Literature (CINAHL), APA PsycNet (encompassing PsycINFO and PsychArticle), Google Scholar, and Africa Journal Online (AJOL) were systematically searched to identify potentially relevant articles. Two independent assessors extracted the data from the eligible studies using a pre-tested and standardized excel spreadsheet. Outcomes were 30-day in-hospital stroke case fatality and associated risk factors. Data was pooled using random effects model. Ninety-three (93) studies involving 42,057 participants were included. The overall stroke case fatality rate was 27% [25%-29%]. Subgroup analysis revealed 24% [21%-28%], 25% [21%-28%], 29% [25%-32%] and 31% [20%-43%] stroke case fatality rates in East Africa, Southern Africa, West Africa, and Central Africa respectively. Stroke severity, stroke type, untyped stroke, and post-stroke complications were identified as risk factors. The most prevalent risk factors were low (<8) Glasgow Coma Scale score, high (≥10) National Institute Health Stroke Scale score, aspiration pneumonia, hemorrhagic stroke, brain edema/intra-cranial pressure, hyperglycemia, untyped stroke (stroke diagnosis not confirmed by neuroimaging), recurrent stroke and fever. The findings indicate that one in every four in-hospital people with stroke in sub-Saharan Africa dies within 30 days of admission. Importantly, the identified risk factors are mostly modifiable and preventable, highlighting the need for context-driven health policies, clinical guidelines, and treatments targeting these factors.


Introduction
Stroke is the second most significant contributor to global mortality and the third most prevalent cause of combined death and disability, accounting for 12% of overall deaths and 6% of combined death and disability [1,2].The incidence of stroke in Africa continues to increase and is among the highest in the world (2), with an age-adjusted incidence rate of 316 per 100,000 people and a prevalence of 1460 per 100,000 people per year [3][4][5].It is estimated that nine out of ten burden of stroke is attributable to modifiable risk factors with regional variations [6].In the sub-Saharan Africa (SSA) region, hypertension, dyslipidemia, regular meat eating, an elevated waist-to-hip ratio, diabetes, a low intake of green leafy vegetables, stress, table salt, heart disease, physical inactivity and tobacco use have been identified as leading risk factors associated with stroke [7][8][9].
In-hospital stroke mortality rates in SSA vary significantly, ranging from 1.5% to 77.8% [10,11].A systematic review and meta-analysis found that 22% of the overall prevalence of inhospital mortality in SSA was linked to stroke [12].However, a considerable number of cohort studies have been published after this review, necessitating a review to comprehensively identify risk factors and capture current estimates of in-hospital stroke mortality in SSA.Thirtyday in-hospital stroke mortality refers to the percentage of people who die within 30 days of being admitted to the hospital for stroke [13].The 30-day mortality rate following hospital admission is a commonly utilized metric to evaluate hospital performance [14].The choice of this standardized timeframe was to ensure an equitable evaluation, mitigating the impact of transfer rates or variations in length of stay on the measurement [14,15].
Generally, acute medical and rehabilitation care for stroke survivors is limited and underfunded in SSA [16].Cost-effective and pragmatic preventative programs aimed at identifying and managing risk factors are thus critical in reducing the burden of stroke in SSA [7].Such efforts could also improve the survival rate of in-patients with high risk of mortality through timely intervention and care [17].Consequently, evaluation of 30-day in-hospital stroke case fatality and associated risk factors in SSA may be an important step to reduce case fatality in this population [13].This is essential to help guide policy formulation and treatment effort to reduce stroke-related case fatality.Therefore, the present study sought to systematically review and update scientific evidence on the 30-day case fatality and associated risk factors among inhospital persons with stroke in SSA.

Protocol registration and best practices
The systematic review and meta-analysis were conducted in accordance with the guidelines outlined in the Preferred Reporting Items for Systematic Review and Meta-Analysis (PRISMA) [18] to ensure the application of best practices.The review was registered with the International Prospective Register of Systematic Reviews (PROSPERO) database (registration number: CRD42021227367).The protocol for this review has been published elsewhere [13].

Eligibility criteria
Studies reporting 30-day in-hospital stroke case-fatality and/or associated risk factors in any SSA country were eligible for inclusion.Persons diagnosed with stroke and on admission in any health facility in SSA, irrespective of age, were included.Reviews, commentaries, letters of correspondence, community studies, systematic reviews and conference papers were excluded.
The primary and secondary outcomes were in-hospital 30-day stroke case fatality rate and risk factors associated with in-hospital 30-day stroke case fatality respectively.

Search methods for identification of studies
Medline (via PubMed), Cumulative Index to Nursing and Allied Health Literature (CINAHL), APA PsycNet (encompassing PsycINFO and PsychArticle), Google Scholar, and African Journals Online (AJOL) were searched for publications on the rate and risk factors of in-hospital stroke mortality/case-fatality in SSA.The search was limited to papers published in English from January 1990 to September 2023.The full search strategy is presented in S1 Table .Hand searches of the reference lists of relevant articles were conducted to identify additional eligible studies.

Screening, selection of studies, and data extraction
All search results were collated and deduplicated using Mendeley reference manager software.Two authors independently (MA and DO) screened the titles and abstracts of all studies against the pre-specified eligibility criteria using a pretested study selection chart.The screening of titles and abstracts was conducted manually.Full texts of all potentially eligible studies were accessed through PubMed, Google Scholar, and the respective websites of individual journals such as Elsevier, Lancet, and Sage, and further assessed for eligibility.Disagreements on inclusion decisions were resolved through discussion between the two reviewers or by consulting a third independent reviewer (USA).Corresponding authors of studies whose full texts were not accessible were contacted through email to provide them.If the full text was still inaccessible and vital information needed to decide on eligibility and inclusion was unavailable the study was excluded.The PRISMA flow chart was used to summarize the study selection process.Two independent reviewers (MA and DO) pilot-tested the data extraction template (a Microsoft Excel sheet) with 10% of the included studies, before commencing data extraction for all the included studies.Collated data included author's last name, year of publication, study country, participants' age, sample size, 30-day in-hospital stroke case fatality and associated risk factors.

Outcome and operationalization
30-day in-hospital stroke case fatality rate was defined as the proportion of persons with stroke who died within 30 days of hospital admission.A risk factor was also defined as a variable that was linked to, or caused the death of, a hospitalized person with stroke within 30 days of admission.

Risk of bias and quality assessment
The methodological quality of the included studies was independently assessed by two reviewers (MA and USA) using a 10-item tool for assessing risk of bias in prevalence studies [19].The tool assesses characteristics of reporting internal and external validity of studies.Risk of bias (low = 7-10; moderate = 4-6; high = 0-3) was classified based on the following items from this tool: national and target population representativeness, sampling frame representativeness, selection method, response bias, data quality, case definition, reliability and validity of study instrument, data collection consistency, prevalence duration, and parameter suitability.Discrepancies in quality and risk rating were resolved by consensus between the two reviewers.

Data synthesis
Extracted data were exported to Stata (version 16; Stata Corp, TX, USA) from Microsoft excel 2013 for statistical analyses.The Clopper-Pearson method [i.e., 'cimethod (exact)'] was used to determine the study-specific confidence intervals [20], to ensure that the resulting intervals always contain valid and permissible values [21].The rate of 30-day in-hospital stroke case fatality was pooled using a random-effects model.Heterogeneity was visually inspected using the forest plot and quantified using both Cochrane's Q statistic and the I 2 statistic [22].The I 2 values were interpreted based on the Higgins and Thompson classification, with 25%, 50% and 75% reflecting low, moderate and high heterogeneity, respectively [22].Sub-group analysis was conducted (where applicable) to examine whether estimates varied according to the potential moderators such as sub-region (West Africa, East Africa, Southern Africa, and Central Africa).A risk factor was eligible for inclusion if it had been adjusted and reported in included studies [23].inappropriate outcome measures, inappropriate study designs and inappropriate sample groups.A total of 1433 full-text articles were retrieved.Six hundred and forty-three studies were conducted outside SSA, three hundred and seven studies were stroke prevalence and incidence studies, two hundred and seventy-one studies reported over 30-day case fatality, a hundred and seven studies were community studies, eleven studies were reviews, and one study was published in French, and hence were also excluded.This resulted in ninety-three studies [11, eligible for inclusion.

Study characteristics
The characteristics of the included studies are presented in Table 1.A total of 42,057 participants were involved in the studies.Studies were published between 2002 and 2023.The sample size ranged from 35 to 12,233 participants.Participants' ages ranged from 16 to 115 years.Most of the studies were conducted in West Africa (43 out of 93 studies), followed by East Africa (42 out of 93 studies).In terms of country, most of the studies were conducted in Nigeria (24 out of 93 studies) and Ethiopia (22 out of 93 studies).

30-day in-hospital stroke case fatality
Table 2 shows the pooled 30-day in-hospital stroke case fatality rate, and the results of the subgroup analysis.

Discussion
The primary aim of this systematic review and meta-analysis was to examine 30-day in-hospital stroke case fatality and associated risk factors in sub-Saharan Africa.A total of 93 hospitalbased retrospective and prospective cohort studies, involving 42,057 persons with stroke across 19 countries in sub-Saharan Africa were included in the review.The meta-analysis of pooled data revealed 27% 30-day in-hospital stroke case fatality rate.Comparatively, this estimate is slightly higher than the pooled estimate of 22% reported in a previous systematic review and meta-analysis [12].A possible explanation for this disparity could be the larger number of studies (i.e., 93 studies) included in the present review, as compared to the previous one (i.e., 27 studies), further highlighting the need for this current review.The 27% case fatality found in the present review is somewhat higher than the trends in high income countries [116].Possible explanations may be the rising incidence of stroke in low-and middle-income countries such as those in SSA, and the limited availability of resources for acute medical and rehabilitation care for persons with stroke in many SSA countries [16,117,118].Sub-group analysis revealed varying rates of 30-day in-hospital stroke case fatality across the SSA regions, with the highest rates found in Central Africa (31%), followed by West Africa (29%), Southern Africa (25%), and East Africa (24%).The observed variance in case fatality rates across sub-regions could be partly explained by the gross domestic product and infrastructural development of each collective sub-region, rather than their individual countries.People's conceptualization of disease and health and their health-seeking behaviors are partly determined by their culture, socioeconomic development of society, and availability of health facilities and services [119].The high case fatality and the variance observed across the subregions found in this current study, may thus be a consequence of sociocultural-economic factors.Delayed hospital visits, admissions and obsolete diagnostic equipment can lead to misdiagnosis and mismanagement, resulting in increased case fatality rates.
An important finding of this review was the risk factors associated with 30-day stroke case fatality in SSA.The identified risk factors (i.e., stroke severity, stroke type, untyped stroke, and post-stroke complications) are similar to findings of previous systematic reviews conducted within the sub-region [12,120].The identified risk for stroke case fatality may be linked to clinical and socioeconomic factors.Hemorrhagic strokes tend to be more severe compared to ischemic strokes and are linked to a significant rise in mortality during the first three months of a stroke.Persons with stroke in SSA are likely to receive an untyped stroke diagnosis due to limited resources including but not limited to financial constraints to afford a requisite diagnostic test such as a Computed Tomography (CT) scan, and lack of available diagnostic facilities and services, increasing susceptibility for misdiagnosis and mismanagement.Many countries in SSA have limited healthcare personnel, frequent CT scan malfunctions, and high costs associated with medical imaging, all of which could contribute to late or failure to confirm the stroke diagnosis [28,34,95], which hampers management.The likely phenomenon of untyped stroke coupled with inability to afford required diagnostic test and lack of diagnostic services likely contributes to the high case fatality in SSA [26,67,93].
Given the relatively high stroke case fatality in SSA, there is a need for effective policies and interventions to minimize the incidence and mortality from stroke at the national, regional, and local levels across countries in SSA.Context-specific health policies to encourage the routine assessment of vulnerable population groups for early detection of risk factors for case fatality is warranted.Additionally, there is the need to enhance public health education and raise awareness about the risk factors for stroke among residents in the region.Improving the healthcare in most SSA countries is essential to addressing in-hospital stroke case fatality.There is thus the need to equip health facilities with the requisite personnel, diagnostic equipment, and procedures to help improve stroke care and rehabilitation within SSA.Such interventions will not only enhance the quality of care provided but also reduce recovery time, hospital stays, and stroke-related fatalities.

Strengths and limitations
This review possesses a number of strengths.The adoption of a well-established systematic review and meta-analysis methodologies, which are aligned with internationally recognized standards and recommendations, coupled with the large number of included studies is a strength of this review.
The review also has some limitations.The studies included in this review exhibited considerable heterogeneity.Readers should therefore be cautious when interpreting the pooled estimates.Emphasis should instead be placed on the distribution in each category and the observed patterns in the data [121].Additionally, the inclusion of only studies published in English, likely underestimate the extent of the case fatality in the sub-region as studies may have been published in other languages.

Conclusion
The present review examined the current scientific evidence regarding 30-day in-hospital stroke case fatality and associated risk factors in sub-Saharan Africa and found 27% stroke case fatality rate.This suggests that at least one in every four hospitalized stroke patients dies within 30 days in SSA.However, most of the identified risk factors for the case fatality were modifiable.This highlights the need for context-tailored health policies, clinical guidelines, and treatment protocols to help with early detection and prevention of risk factors to minimize in-hospital stroke mortality in SSA.

Fig 1 Fig 1 .
Fig 1 shows the data identification process.A total of 13674 potentially relevant studies were identified in literature search and additional hand searches.After removal of duplicates, the abstract of 6151 studies were screened for eligibility based on the inclusion criteria.After screening of abstracts, 4718 studies were excluded.Common reasons for exclusion were Fig 2 shows the publication bias.Inspection of the funnel plot [Fig 2] showed no publication bias.